Search Results (125)

In the realm of shared autonomous vehicle ride-sharing, precise demand prediction is vital for optimizing resource allocation, improving travel efficiency, and promoting sustainable transport solutions. However, existing studies tend to overlook social attributes and demographic characteristics across various regions, resulting in disparities in prediction fairness between areas with plentiful and limited transportation resources. In order to achieve more accurate and fair prediction, an innovative Social Graph Convolution Long Short-Term Memory framework is proposed, incorporating demographic, spatial, and transportation accessibility information into multiple functional graphs, including functional similarity, population structure, and historical demand graphs. Furthermore, Mean Percentage Error indicators are employed in the loss function to balance prediction accuracy and fairness. The findings indicate that there is an enhancement in both prediction accuracy and fairness by at least 8.9% and 12.9%, respectively, compared to base models. Additionally, the predictions for rush hours in both privileged and underprivileged regions exhibit greater precision and rationality, supporting sustainable transport practices. The proposed framework effectively captures the demands of diverse social groups, thereby contributing to the advancement of social equity and long-term sustainability in urban mobility. Full article

Based on the available statistical data, most employees travel by car, which results in traffic jams, hurts the environment, and increases household expenditures. As a result of this phenomenon, our investigation aimed to present the global trends in urban mobility. It is also considered preliminary research of Hungarian transportation habits and trends by summarizing the standard fees of taxi service, parking, and maintaining a driving license. As our research methodology, we chose a systematic literature search and a summary of the available secondary data to present the main global trends characterizing urban mobility and the standards typical of Hungarian transport. The results enable the expansion of the research and provide an opportunity to learn about consumers’ attitudes at the national level. Full article

Traffic-related air pollution (TRAP) is a significant risk to human health and is particularly damaging to children as a vulnerable group. TRAP exposure near schools and on the school commute is linked to a growing number of adverse health effects, including respiratory and cardiovascular disease and can lead to (and exacerbate existing) respiratory conditions. The current study aimed to assess the effectiveness of interventions for the reduction of potential child exposure to TRAP at the school gates and on the school commute. This study employed dispersion modelling to assess the effects of interventions for reducing TRAP concentrations in the vicinity of five schools in England. The results revealed that all interventions led to reductions in nitrogen dioxide (NO₂) concentrations. Improved travel routes were the most effective intervention for reducing concentrations along travel routes, while the introduction of low-emission zones (LEZs) proved most effective in reducing NO₂ concentrations at schools, with greater effectiveness observed at shorter distances. Active travel also demonstrated effectiveness, particularly in areas with heavy traffic. When considering all receptors, LEZ implementation, active travel, and rideshare interventions exhibited effectiveness, with greater distance providing greater reductions in NO₂ concentrations. Anti-idling was found to be more effective in sparsely populated areas. Combined with improved travel routes, anti-idling showed the greatest percentage difference in concentrations, followed by active travel, and rideshare. Full article

Travel safety for women is a concern, particularly in India, where gender-based violence and harassment are significant issues. This study examines how the perception of safety influences women’s travel behaviour and assesses the potential of technology solutions to ensure their safety. Additionally, it explores how AI and machine learning techniques may be leveraged to enhance women’s travel safety. A comprehensive mobility survey was designed to uncover the complex relationship between travel behaviour, reasons for mode choice, built environment, feelings, future mobility, and technological solutions. The responses revealed that security and safety are the most critical factors affecting women’s travel mode choices, with 54% and 41%, respectively. Moreover, over 80% of women indicated a willingness to change their travel behaviour after experiencing fear, anxiety, or danger during their everyday journeys. Participants were 24% less willing to use ride-sharing services than ride-hailing services, which could affect the transition towards more sustainable transportation options. Furthermore, AI-based sentiment analysis revealed that 46% of the respondents exhibited signs of ‘anger’ regarding what could help women feel safer in transient environments. The practical implications of this study’s findings are discussed, highlighting the potential of AI to enhance travel safety and optimise future sustainable transport planning. Full article

Travel time variability (TTV) is a crucial indicator of transportation network performance, assessing travel time reliability and delays. This study investigates TTV metrics within the context of shared mobility using probe data from transportation network companies (TNCs) in Chicago, Los Angeles, and Dallas–Fort Worth. Eight reliability metrics are analyzed and compared for each origin–destination (OD) pair in the network, including standard deviation (SD), the Planning Time Index (PTI), the Travel Time Index (TTI), the Buffer Index (BI), On-time Measures PR (alpha), and the Misery Index (MI), to evaluate their effectiveness in clustering OD pairs using K-means clustering. The findings confirm that SD, PTI, and MI are particularly effective in measuring travel time reliability and clustering within urban systems. This study identifies the most unbalanced supply–demand OD pairs/regions in each city, noting that low/medium-SD clusters around metropolitan airports indicate stable travel times even in high-demand zones, while high-SD clusters in downtown areas reveal significant traffic demands and unreliability. These patterns become more pronounced in study areas with multiple city centers. This study highlights the need for targeted strategies to enhance travel time reliability, particularly in regions like Dallas–Fort Worth, where public transportation alternatives are limited. Full article

Any sharing mobility service aims to make urban mobility sustainable to help reduce environmental impacts and improve the quality of life for all in cities. Many transport services are not currently self-sustainable. The Life for Silver Coast (LifeSC) opened its mobility services on 22 May 2021 and offered electric mobility services during the summer for a few cities in Tuscany. E-bikes and e-scooters can be financially neutral, and even profitable, thanks to the low costs of the vehicles, but they only see a high utilization rate in winter. Shared electric cars, meanwhile, are not profitable. A new shared service that is viable must be profitable to become widely adopted and significantly contribute to sustainability. A few key characteristics have been identified, and one has been tested with a new business model that combines ride-sharing and car-sharing. The innovative Ride Sharing Algorithm (RSA) has been tested based on data from a potential city, Monterondo, where many commuters travel daily to Rome by train. The Italian census and local survey data allowed for the simulation of the scheduling of vehicle rides and an evaluation of the economic results, which could be positive if enough interest for such a system exists among the people, as at least 400 commuters from Monterotondo go to the train station daily in the morning and return in the afternoon. Such a transport demand would justify a new commercial sharing service by using the model tested with the RSA algorithm. Full article

There are various types of sharing economy services, such as ride-sharing and shared-taxi rides. Motivated by these services, we consider a single-server queue in which customers probabilistically select the type of service, that is, the single service or batch service, or other services (e.g., train). In the proposed model, which is denoted by the M+M(K)/M/1 queue, we assume that the arrival process of all the customers follows a Poisson distribution, the batch size is constant, and the common service time (for the single- and batch-service customers) follows an exponential distribution. In this model, the derivation of the sojourn time distribution is challenging because the sojourn time of a batch-service customer is not determined upon arrival but depends on single customers who arrive later. This results in a two-dimensional recursion, which is not generally solvable, but we made it possible by utilizing a special structure of our model. We present an analysis using a quasi-birth-and-death process, deriving the exact and approximated sojourn time distributions (for the single-service customers, batch-service customers, and all the customers). Through numerical experiments, we demonstrate that the approximated sojourn time distribution is sufficiently accurate compared to the exact sojourn time distributions. We also present a reasonable approximation for the distribution of the total number of customers in the system, which would be challenging with a direct-conventional method. Furthermore, we presented an accurate approximation method for a more general model where the service time of single-service customers and that of batch-service customers follow two distinct distributions, based on our original model. Full article

As society becomes increasingly concerned with sustainable development, the demand for high-efficiency, low-cost, and green technology makes air–land multimodal transportation one of the effective means of fast freight transportation. In the actual transportation business, some orders will have overlapping transportation routes, and transporting each order separately will result in resource waste, high costs, and carbon emissions. This paper proposes a multimodal transportation scheme optimization model considering order consolidation to improve transport efficiency and reduce costs and carbon emissions. An improved genetic algorithm incorporating the ride-sharing scheduling method is designed to solve the model. The results show that order consolidation will reduce multimodal transport costs and carbon emissions but increase transportation time slightly, and the advantages in cost and carbon emission reduction will vary with origin–destination scenarios, which are ranked in order of single-origin single-destination, single-origin multi-destinations, multi-origin single-destination, and multi-origin multi-destination. For the fourth scenario, the cost and carbon emissions decrease by 16.6% and 26.69%, respectively, and the time increases by 5.56% compared with no consolidation. For the sensibility of customer demands, it is found that order consolidation has the advantage for price-sensitive, time- and price-sensitive, and time- and carbon emission-sensitive customers; however, it is specifically beneficial for time-sensitive customers only in single-origin single-destination scenarios. Full article

Human beings have created numerous laws, sayings and proverbs that still influence behaviors and decision-making processes of people. Some of the laws, sayings or proverbs are used by people to understand the phenomena that may take place in daily life. For example, Murphy’s law states that “Anything that can go wrong will go wrong.” Murphy’s law is helpful for project planning with analysis and the consideration of risk. Similar to Murphy’s law, the old saying “Two heads are better than one” also influences the determination of the ways for people to get jobs done effectively. Although the old saying “Two heads are better than one” has been extensively discussed in different contexts, there is a lack of studies about whether this saying is valid and can be applied in evolutionary computation. Evolutionary computation is an important optimization approach in artificial intelligence. In this paper, we attempt to study the validity of this saying in the context of evolutionary computation approach to the decision making of ridesharing systems with trust constraints. We study the validity of the saying “Two heads are better than one” by developing a series of self-adaptive evolutionary algorithms for solving the optimization problem of ridesharing systems with trust constraints based on the saying, conducting several series of experiments and comparing the effectiveness of these self-adaptive evolutionary algorithms. The new finding is that the old saying “Two heads are better than one” is valid in most cases and hence can be applied to facilitate the development of effective self-adaptive evolutionary algorithms. Our new finding paves the way for developing a better evolutionary computation approach for ridesharing recommendation systems based on sayings created by human beings or human intelligence. Full article

The transportation landscape is witnessing profound changes due to technological advancements, necessitating proactive policy responses to harness innovation and avert urban mobility disruption. The sharing economy has already transformed ridesharing, bicycle-sharing, and electric scooters, with shared autonomous vehicles (SAVs) poised to reshape car ownership. This study pursues two objectives: firstly, to establish a market segmentation for shared ride services and secondly, to evaluate the environmental impact of ridesharing in different contexts. To mitigate potential biases linked to stated preference data, we analysed the navette service, utilized by a research institute in Europe, closely resembling future SAVs. The market segmentation relied on hierarchical cluster analysis using employee survey responses, while the environmental analysis was grounded in the 2019 navette service data. Our analysis revealed four unique employee clusters: Cluster 1, emphasizing active transportation and environmental awareness; Cluster 2, showing openness towards SAVs given reliable alternatives are available; Cluster 3, the largest segment, highlighting a demand for policy support and superior service quality; and Cluster 4, which places a premium on time, suggesting a potential need for strategies to make the service more efficient and, consequently, discourage private car use. These findings highlight a general willingness to adopt shared transport modes, signalling a promising transition to shared vehicle ownership with significant environmental benefits achievable through service design and policy measures. Full article

With the emergence of the sharing economy, a significant change in consumer behavior can be observed worldwide, which has a considerable impact on various industries. The rise of the sharing economy has changed the way people experience transport services, with ridesharing being a catalyst for change. In Romania, the debut of Uber in 2015 sparked controversy and led to legal regulations that were adapted to local specificities, highlighting the adaptability of ridesharing platforms to different legal frameworks. In the context of this development, the views and perceptions of Generation Z will be crucial in determining the direction in which this conflict between disruptive models and traditional players in the transport sector develops. The article deals with business models based on collaborative consumption, with a focus on ridesharing, and examines the attitudes, perceptions, and behavior of Romanian youths (aged 18–26) towards these models. The aim of the study is to determine the opinion of young Romanians on collaborative consumption in transport services—ridesharing (Uber case)—and their attitude towards the ethical controversies related to Uber’s business model. A quantitative research approach was chosen, and an exploratory study was conducted using a questionnaire, with the non-probabilistic sample consisting of relevant observation units aged 18–26 years. The results show that almost 90% of the young Romanians surveyed use Uber and are satisfied with the quality, convenience, and speed of the service. Despite the positive attitude, there is a paradoxical tendency among respondents to regulate ridesharing services in a similar way to traditional taxis. Ethical considerations show that respondents tend to neutralize perceptions and justify the emergence of new models as normal and beneficial for competition and consumers. Full article

Ridesharing effectively tackles urban mobility challenges by providing a service comparable to private vehicles while minimising resource usage. Our research primarily concentrates on dynamic ridesharing, which conventionally involves connecting drivers with passengers in need of transportation. The process of one-to-one matching presents a complex challenge, particularly when addressing it on a large scale, as the substantial number of potential matches make the attainment of a global optimum a challenging endeavour. This paper aims to address the absence of an optimal approach for dynamic ridesharing by refraining from the conventional heuristic-based methods commonly used to achieve timely solutions in large-scale ride-matching. Instead, we propose a novel approach that provides snapshot-optimal solutions for various forms of one-to-one matching while ensuring they are generated within an acceptable timeframe for service providers. Additionally, we introduce and solve a new variant in which the system itself provides the vehicles. The efficacy of our methodology is substantiated through experiments carried out with real-world data extracted from the openly available New York City taxicab dataset. Full article

Future adoption of shared automated vehicles (SAVs) should lead to several societal benefits, but both automated vehicles (AVs) and ridesharing must overcome their barriers to acceptance. Previous research has investigated age differences in ridesharing usage and factors influencing the acceptability and acceptance of AVs. Further complicating our understanding of SAV acceptance, much of the public lack accurate knowledge and/or actual experience regarding AVs. In this study, we employed a 3 (age group) × 4 (video condition) longitudinal mixed experimental design to investigate age differences in anticipated SAV acceptance after viewing different types of introductory videos related to AVs (educational, experiential, or both) or currently available ridesharing provided by transportation network companies (control). Younger, middle-aged, and older adults were randomly assigned to watch (1) an educational video about SAV technologies and potential benefits, (2) an experiential video showing an SAV navigating traffic, (3) both the experiential and educational videos or (4) a control video explaining how current ridesharing services work. Attitudes toward SAVs (intent to use, trust/reliability, perceived usefulness, perceived ease of use, safety, desire for control, cost, authority, media, and social influence) were measured before and after viewing the video(s). Significant differences in how SAV attitudes changed were found between the educational and experiential video conditions relative to the control video and between different age groups. Findings suggest that educational and/or experiential videos delivered in an online format can have modest but significant improvements to their viewers’ attitudes toward SAVs—particularly those of older adults. Full article

The core issue in ridesharing is designing reasonable algorithms to match drivers and passengers. The ridesharing matching problem, influenced by various constraints such as weather, traffic, and supply–demand dynamics in real-world scenarios, requires optimization of multiple objectives like total platform revenue and passenger waiting time. Due to its complexity in terms of constraints and optimization goals, the ridesharing matching problem becomes a central issue in the field of mobile transportation. However, the existing research lacks exploration into the fairness of driver income, and some algorithms are not practically applicable in the industrial context. To address these shortcomings, we have developed a fairness-oriented dynamic matching algorithm for ridesharing, effectively optimizing overall platform efficiency (expected total driver income) and income fairness among drivers (entropy of weighted amortization fairness information between drivers). Firstly, we introduced a temporal dependency of matching outcomes on subsequent matches in the scenario setup and used reinforcement learning to predict these temporal dependencies, overcoming the limitation of traditional matching algorithms that rely solely on historical data and current circumstances for order allocation. Then, we implemented a series of optimization solutions, including the introduction of a time window matching model, pruning operations, and metric representation adjustments, to enhance the algorithm’s adaptability and scalability for large datasets. These solutions also ensure the algorithm’s efficiency. Finally, experiments conducted on real datasets demonstrate that our fairness-oriented algorithm based on reinforcement learning achieves improvements of 81.4%, 28.5%, and 79.7% over traditional algorithms in terms of fairness, platform utility, and matching efficiency, respectively. Full article

Autonomous vehicles are anticipated to revolutionize ride-sharing services and subsequently enhance the public transportation systems through a first–last-mile transit service. Within this context, a fleet of autonomous vehicles can be modeled as a Dial-a-Ride Problem with certain features. In this study, we propose a holistic solving approach to this problem, which combines the mixed-integer linear programming formulation with a novel graph dimension reduction method based on the graph embedding framework. This latter method is effective since accounting for heterogeneous travel demands of the covered territory tends to increase the size of the routing graph drastically, thus rendering the exact solving of small instances computationally infeasible. An application is provided for the real transport demand of the industrial district of “Vallée de la Chimie” in Lyon city, France. Instances involving more than 50 transport requests and 10 vehicles could be easily solved. Results suggest that this method generates routes of reduced nodes with lower vehicle kilometers traveled compared to the constrained K-means-based reduction. Reductions in terms of GHG emissions are estimated to be around 75% less than the private vehicle mode in our applied service. A sensitivity analysis is also provided. Full article